{"total":446,"rows":[{"id":"2306.12059","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","created":"2023-06-21","cited_by_count":204,"area":null},{"id":"2302.03655","title":"Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs","created":"2023-02-07","cited_by_count":117,"area":null},{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90,"area":null},{"id":"2502.12147","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","created":"2025-02-17","cited_by_count":84,"area":null},{"id":"2209.10702","title":"SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials","created":"2022-09-21","cited_by_count":82,"area":null},{"id":"2305.05577","title":"FAENet: Frame Averaging Equivariant GNN for Materials Modeling","created":"2023-04-28","cited_by_count":73,"area":null},{"id":"2312.07511","title":"A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems","created":"2023-12-12","cited_by_count":68,"area":null},{"id":"2402.08871","title":"Position: Topological Deep Learning is the New Frontier for Relational Learning","created":"2024-02-14","cited_by_count":66,"area":null},{"id":"2410.22570","title":"Orb: A Fast, Scalable Neural Network Potential","created":"2024-10-29","cited_by_count":66,"area":null},{"id":"2306.06482","title":"TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials","created":"2023-06-10","cited_by_count":54,"area":null},{"id":"2502.14546","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","created":"2025-02-20","cited_by_count":53,"area":null},{"id":"2410.12771","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","created":"2024-10-16","cited_by_count":52,"area":null},{"id":"2410.23179","title":"Does equivariance matter at scale?","created":"2024-10-30","cited_by_count":52,"area":null},{"id":"2305.18415","title":"Geometric Algebra Transformer","created":"2023-05-28","cited_by_count":48,"area":null},{"id":"2304.04757","title":"A new perspective on building efficient and expressive 3D equivariant graph neural networks","created":"2023-04-07","cited_by_count":47,"area":null},{"id":"2310.03121","title":"OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials","created":"2023-10-04","cited_by_count":47,"area":null},{"id":"2410.22388","title":"ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation","created":"2024-10-29","cited_by_count":43,"area":null},{"id":"2310.02970","title":"Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space","created":"2023-10-04","cited_by_count":41,"area":null},{"id":"2210.11250","title":"Structure-based drug design with geometric deep learning","created":"2022-10-19","cited_by_count":40,"area":null},{"id":"2306.07473","title":"3D molecule generation by denoising voxel grids","created":"2023-06-13","cited_by_count":40,"area":null},{"id":"2410.24169","title":"The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains","created":"2024-10-31","cited_by_count":40,"area":null},{"id":"2310.04292","title":"Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets","created":"2023-10-06","cited_by_count":39,"area":null},{"id":"2310.16802","title":"From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction","created":"2023-10-25","cited_by_count":39,"area":null},{"id":"2302.06594","title":"Geometric Clifford Algebra Networks","created":"2023-02-13","cited_by_count":38,"area":null},{"id":"2308.10364","title":"SE(3) Equivariant Augmented Coupling Flows","created":"2023-08-20","cited_by_count":37,"area":null},{"id":"2401.10216","title":"Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products","created":"2024-01-18","cited_by_count":36,"area":null},{"id":"2308.14920","title":"Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions","created":"2023-08-28","cited_by_count":35,"area":null},{"id":"2304.10061","title":"Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size","created":"2023-04-20","cited_by_count":33,"area":null},{"id":"2402.07472","title":"Cartesian atomic cluster expansion for machine learning interatomic potentials","created":"2024-02-12","cited_by_count":31,"area":null},{"id":"2208.12590","title":"Ab-initio quantum chemistry with neural-network wavefunctions","created":"2022-08-26","cited_by_count":29,"area":null},{"id":"2212.05517","title":"SchNetPack 2.0: A neural network toolbox for atomistic machine learning","created":"2022-12-11","cited_by_count":29,"area":null},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2301.11355","title":"Rigid Body Flows for Sampling Molecular Crystal Structures","created":"2023-01-26","cited_by_count":27,"area":null},{"id":"2402.12365","title":"Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators","created":"2024-02-19","cited_by_count":27,"area":null},{"id":"2306.09549","title":"QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules","created":"2023-06-15","cited_by_count":26,"area":null},{"id":"2408.15165","title":"Latent Ewald summation for machine learning of long-range interactions","created":"2024-08-27","cited_by_count":26,"area":null},{"id":"2503.09814","title":"A practical guide to machine learning interatomic potentials -- Status and future","created":"2025-03-12","cited_by_count":26,"area":null},{"id":"2305.14247","title":"Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science","created":"2023-05-23","cited_by_count":25,"area":null},{"id":"2402.14730","title":"Clifford-Steerable Convolutional Neural Networks","created":"2024-02-22","cited_by_count":25,"area":null},{"id":"2410.02698","title":"Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups","created":"2024-10-03","cited_by_count":24,"area":null},{"id":"2412.11569","title":"The dark side of the forces: assessing non-conservative force models for atomistic machine learning","created":"2024-12-16","cited_by_count":24,"area":null},{"id":"2309.15123","title":"Uncovering Neural Scaling Laws in Molecular Representation Learning","created":"2023-09-15","cited_by_count":23,"area":null},{"id":"2310.18278","title":"Navigating protein landscapes with a machine-learned transferable coarse-grained model","created":"2023-10-27","cited_by_count":23,"area":null},{"id":"2302.05743","title":"Is Distance Matrix Enough for Geometric Deep Learning?","created":"2023-02-11","cited_by_count":22,"area":null},{"id":"2405.15429","title":"E(n) Equivariant Topological Neural Networks","created":"2024-05-24","cited_by_count":22,"area":null},{"id":"2501.13986","title":"An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks","created":"2025-01-23","cited_by_count":22,"area":null},{"id":"2607.05155","title":"EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments","created":"2026-07-06","cited_by_count":20,"area":null},{"id":"2306.01474","title":"Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning","created":"2023-06-02","cited_by_count":19,"area":null},{"id":"2406.05753","title":"Grounding Continuous Representations in Geometry: Equivariant Neural Fields","created":"2024-06-09","cited_by_count":19,"area":null},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19,"area":null},{"id":"2403.09549","title":"Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields","created":"2024-03-14","cited_by_count":18,"area":null},{"id":"2410.11443","title":"Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?","created":"2024-10-15","cited_by_count":18,"area":null},{"id":"2210.04225","title":"Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials","created":"2022-10-09","cited_by_count":17,"area":null},{"id":"2406.03164","title":"Topological Neural Networks go Persistent, Equivariant, and Continuous","created":"2024-06-05","cited_by_count":17,"area":null},{"id":"2504.16068","title":"High-performance training and inference for deep equivariant interatomic potentials","created":"2025-04-22","cited_by_count":17,"area":null},{"id":"2306.00091","title":"A General Framework for Equivariant Neural Networks on Reductive Lie Groups","created":"2023-05-31","cited_by_count":16,"area":null},{"id":"2406.13864","title":"Evaluating representation learning on the protein structure universe","created":"2024-06-19","cited_by_count":16,"area":null},{"id":"2410.09667","title":"EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants","created":"2024-10-12","cited_by_count":16,"area":null},{"id":"2310.02428","title":"EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations","created":"2023-10-03","cited_by_count":15,"area":null},{"id":"2402.17660","title":"TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2404.07194","title":"VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification","created":"2024-04-10","cited_by_count":15,"area":null},{"id":"2405.15389","title":"Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing","created":"2024-05-24","cited_by_count":15,"area":null},{"id":"2503.23794","title":"Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks","created":"2025-03-31","cited_by_count":15,"area":null},{"id":"2506.13523","title":"The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products","created":"2025-06-16","cited_by_count":15,"area":null},{"id":"2406.02313","title":"Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models","created":"2024-06-04","cited_by_count":14,"area":null},{"id":"2502.19227","title":"Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems","created":"2025-02-26","cited_by_count":14,"area":null},{"id":"2211.16684","title":"Capturing long-range interaction with reciprocal space neural network","created":"2022-11-30","cited_by_count":13,"area":null},{"id":"2302.14231","title":"CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling","created":"2023-02-28","cited_by_count":13,"area":null},{"id":"2306.15065","title":"Molecular geometric deep learning","created":"2023-06-22","cited_by_count":13,"area":null},{"id":"2311.04744","title":"Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers","created":"2023-11-08","cited_by_count":13,"area":null},{"id":"2404.07181","title":"A predictive machine learning force field framework for liquid electrolyte development","created":"2024-04-10","cited_by_count":13,"area":null},{"id":"2405.14253","title":"Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2406.17747","title":"Probing the effects of broken symmetries in machine learning","created":"2024-06-25","cited_by_count":13,"area":null},{"id":"2410.17878","title":"Relaxed Equivariance via Multitask Learning","created":"2024-10-23","cited_by_count":13,"area":null},{"id":"2504.02367","title":"Reinforcement Fine-Tuning for Materials Design","created":"2025-04-03","cited_by_count":13,"area":null},{"id":"2305.15603","title":"Learning Lagrangian Fluid Mechanics with E($3$)-Equivariant Graph Neural Networks","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2501.19216","title":"E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products","created":"2025-01-31","cited_by_count":12,"area":null},{"id":"2505.19350","title":"FlashMD: long-stride, universal prediction of molecular dynamics","created":"2025-05-25","cited_by_count":12,"area":null},{"id":"2509.12178","title":"All that structure matches does not glitter","created":"2025-09-15","cited_by_count":12,"area":null},{"id":"2509.21624","title":"HIP: Hessian Interatomic Potentials without derivatives","created":"2025-09-25","cited_by_count":12,"area":null},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11,"area":null},{"id":"2402.06275","title":"Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics","created":"2024-02-09","cited_by_count":11,"area":null},{"id":"2406.17295","title":"Less can be more for predicting properties with large language models","created":"2024-06-25","cited_by_count":11,"area":null},{"id":"2502.01171","title":"Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity","created":"2025-02-03","cited_by_count":11,"area":null},{"id":"2601.16195","title":"Pushing the limits of unconstrained machine-learned interatomic potentials","created":"2026-01-22","cited_by_count":11,"area":null},{"id":"2604.09130","title":"EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers","created":"2026-04-10","cited_by_count":11,"area":null},{"id":"2210.01705","title":"Tensor-reduced atomic density representations","created":"2022-10-02","cited_by_count":10,"area":null},{"id":"2301.13821","title":"Complete Neural Networks for Complete Euclidean Graphs","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2302.05823","title":"Data efficiency and extrapolation trends in neural network interatomic potentials","created":"2023-02-12","cited_by_count":10,"area":null},{"id":"2411.09429","title":"AI-driven inverse design of materials: Past, present and future","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2411.09678","title":"NeuralDEM -- Real-time Simulation of Industrial Particulate Flows","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2503.01431","title":"How simple can you go? An off-the-shelf transformer approach to molecular dynamics","created":"2025-03-03","cited_by_count":10,"area":null},{"id":"2505.13631","title":"Learning (Approximately) Equivariant Networks via Constrained Optimization","created":"2025-05-19","cited_by_count":10,"area":null},{"id":"2303.12853","title":"Three iterations of $(d-1)$-WL test distinguish non isometric clouds of $d$-dimensional points","created":"2023-03-22","cited_by_count":9,"area":null},{"id":"2307.09337","title":"Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions","created":"2023-07-15","cited_by_count":9,"area":null},{"id":"2402.02681","title":"Equivariant Symmetry Breaking Sets","created":"2024-02-05","cited_by_count":9,"area":null},{"id":"2404.10935","title":"Molecular relaxation by reverse diffusion with time step prediction","created":"2024-04-16","cited_by_count":9,"area":null},{"id":"2405.19276","title":"A Recipe for Charge Density Prediction","created":"2024-05-29","cited_by_count":9,"area":null},{"id":"2409.17622","title":"Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs","created":"2024-09-26","cited_by_count":9,"area":null},{"id":"2507.14302","title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","created":"2025-07-18","cited_by_count":9,"area":null}],"next_cursor":"100"}